Language minority students with poor and good reading comprehension : reading-related processes and use of reading strategies
Bibliographic record
Abstract
Few studies have focused on the comprehension performance of language minority (LM) students and their findings are mixed. Some studies show that reading comprehension is an area of weakness and academic difficulty for LM students whereas others show the language status does not make a difference in reading comprehension performance. Various sources of difficulty LM students experience in reading comprehension have been described. The primary purpose of this study was to examine the similarities and differences between LM good and poor reading comprehenders in terms of their reading-related linguistic and cognitive skills, and their use of reading strategies. A descriptive multiple-case study comparing three poor reading comprehenders to six good reading comprehenders attending grade 6 and matched by gender, age, school, years of schooling in English, and years living in Canada was conducted. Comparisons were made in relation to their language proficiency, word-level reading skills, verbal working memory, and their use of reading strategies. The results indicated that the LM poor reading comprehenders obtained lower scores in measures of morphological awareness skills, word reading accuracy and efficiency, and vocabulary when compared to the good comprehenders. No differing patterns were found for non-word reading skills, syntactic awareness and working memory. As for the use of reading strategies, the poor and good comprehenders used reading strategies before, during and after a reading activity. The good reading comprehenders tended to use global reading strategies more frequently whereas the poor reading comprehenders tended to use support reading strategies more often. No difference was found between the poor and good comprehenders in the use of problem solving reading strategies. Having an explicit goal to perform after reading did not help the overall reading comprehension performance of participants but it promoted their use of reading strategies. Weaknesses in reading basic skills and higher-order skills seem to be sources of difficulty related to the reading comprehension failures of LM struggling comprehenders in this study. Future research should examine whether and how the direct teaching of morphological skills and strategic reading could help the reading comprehension performance of LM students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".